FP&A AI Architect

Gusto's Finance team is rebuilding planning, forecasting, and analysis with AI at the center. As Staff FP&A AI Architect, you'll design and lead the technical and process architecture that turns FP&A from a manual, spreadsheet-driven cycle into an automated, insight-generating function.

Responsibilities:

• Set the strategy: Define the multi-year architecture and roadmap for AI-enabled planning, forecasting, and reporting, prioritizing use cases by value, feasibility, and risk.
• Build forecasting systems: Architect driver-based, ML-assisted forecasts for revenue, headcount, opex, and cash, combining statistical models with LLM workflows and benchmarking against human forecasts.
• Automate analysis: Build agentic workflows for variance commentary, flux analysis, scenario modeling, board and QBR narratives, and ad-hoc 'ask the numbers' questions.
• Own the finance data foundation: Work with Data Engineering on governed data models connecting the GL, billing, CRM, HRIS, and planning tools, so AI outputs are traceable and auditable.
• Embed controls and trust: Establish evals, human-in-the-loop checkpoints, lineage, and audit trails that meet SOX, security, and privacy requirements.
• Evaluate and select tools: Drive build-vs-buy decisions across planning platforms, LLM providers, and orchestration frameworks.
• Lead through influence: Mentor analysts and engineers, create reusable patterns and templates, and drive AI adoption across Finance.
• Measure impact: Report on cycle-time reduction, forecast accuracy, analyst capacity freed, and decision latency.

Requirements:

• 10+ years in FP&A, corporate finance, finance systems, data science, or finance engineering, including 3+ years designing or leading AI/ML or advanced automation for finance
• Strong FP&A fundamentals: driver-based modeling, operating plans, rolling forecasts, SaaS or fintech metrics, variance analysis
• Hands-on experience with LLMs and agentic systems (prompting, RAG, tool use, evals) and classical forecasting (time series, regression, ensembles)
• Proficiency in SQL and Python, a modern data stack (e.g., Snowflake, Databricks, dbt), and BI tools
• Experience with enterprise planning platforms and ERP/GL systems
• Track record shipping production-grade solutions under SOX, data governance, and security review
• Exceptional communication: able to explain model behavior to a CFO and system design to an engineer

Nice to have:

• Payroll, HR tech, fintech, or SaaS finance experience
• Experience building internal tools with Engineering
• Familiarity with MCP-style integrations and AI governance frameworks
• CPA, CFA, or an advanced quantitative degree